\section{Literature Review}
\paragraph{}Jurca and Faltings discuss reputation mechanisms in the 
marketplace.  Their RM records the feedback of a client only when the 
provider claims good service.   They explain that if there are 
contradictory reports (the provider claims good service, but the client 
submits negative feedback), then this means that one of the parties is 
lying.  \cite[p. 3]{Jurca2007b}.  As a result, their RM sanctions both 
the provider and the client.  However, if the provider acknowledges 
failure, they regard this as ``honest'' behavior and claim that 
``failures occur despite best effort, and by acknowledging them, the 
provider shouldn't suffer.'' \cite[p. 3]{Jurca2007b}

\paragraph{}In our project, if the student submitting an assignment is 
the ``provider'' and the student grading the assignment is the 
``client,'' it would mean that submitting different grades would result 
in a negative change to both students' reputation scores.  In addition, 
it would mean that if students acknowledged failure (or a low score) 
their reputation would not be punished.  In CLAPTRAP, we reject the 
decrease of both reputation scores and instead give preference to the 
individual with the highest rank.  If a student gives herself a low 
score, and the grader agrees with this score, then her reputation is not 
penalized.  Jurca and Faltings’ model applies imperfectly, because they 
assume ``perfect delivery channels, such that the client perceives 
exactly the same quality as the provider.'' \cite[p. 7]{Jurca2007b}.  In 
CLAPTRAP, this would be the same as assuming that each students 
perceives grades that should be given in an objective way.  In the 
marketplace, it is easier to be objective about quality of service (did 
the package arrive on time or not?), whereas in academic grading there 
are more subjective possibilities.  A strict rubric for each assignment 
would improve the applicability of this RM.

\paragraph{}Jurca and Faltings also discuss reputation in relation to 
incentive for honest or dishonest feedback. \cite[p. 3]{Jurca2005b}.  
They explain how payoffs for reporting results in a Nash equilibrium, 
but they also explain that lying schemes can also have Nash equilibrium.  
Their payoff system works basically by comparing reports and rewarding 
those with consistent reports.  Unfortunately, the reputation mechanism 
cannot determine whether the reports are true or false.  They propose a 
``method of enforcing the selection of the truthful strategy based on 
trusted reports (i.e. verifiable reports coming from specialized 
reporters.)'' \cite[p. 3]{Jurca2005b}. Other feedback can then be 
evaluated based on these trusted reports.  This eliminates undesired 
equilibrium points.

\paragraph{}This is the premise of CLAPTRAP: that expert opinion 
injected randomly as a basis for comparison of feedback will result in a 
better RM and would reduce incentives to lie.  However, Jurca and 
Faltings do not address the consequences of a change in the reputability 
of the ``trusted sources,'' which is a consideration in CLAPTRAP since 
TA’s develop a low reputation as well as students.

\paragraph{}Papaioannou and Stamoulis discuss a reputation model which 
encourages truthful feedback in peer-to-peer systems.  
\cite{Papaioannou}  Like Jurca and Faltings, they propose reputation 
penalties for peers who offer contradicting feedback, but they also 
implement a system that allows for reputation recovery and a punishment 
system for disagreeing reports.  If $x$ is the penalty for disagreeing 
reports, and $y$ is the reward for agreeing reports, then they claim 
that $0<y<x$.  Thus, $x : y $ determines the speed of restoring a 
non-credible reporting behavior.  \cite[p. 3]{Papaioannou}.  If under 
punishment (for disagreeing feedback) a peer is not allowed to transact 
with others.  ``The severity of each peer's punishment is determined by 
his corresponding non-credibility metric; this is maintained by the 
mechanism and evolves according to the peer's record.''  Also, during 
the ``punishment period,'' a peer's feedback is not taken into account 
by the RM.  \cite[p. 2]{Papaioannou}.  A ``punishment period'' is not 
very applicable in an anonymous grading system (because not being 
``allowed'' to grade assignments might actually create incentive to 
develop a low reputation).  In a marketplace or peer-to-peer system that 
operates like a market, one is not obligated to participate.  However, 
our project will include a private threshold for reputations that would 
prevent those with outlier reputations from affecting the RM (and 
student grades).

\paragraph{}Jin \textit{et al.} discuss a reputation system in an 
educational environment, but it is based on a positive-filter analysis 
of collaboration and usage of an on line education center (including 
blogs, wikis, and resource centers).\cite{Papaioannou}.    The article 
discusses an algorithm used for calculating ranks based on interaction 
with these resources, but there is no penalty for faulty work and less 
risk of moral hazard than exists in an anonymous grading system.

